IPL Action Analysis-2023¶

In [1]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
In [2]:
df = pd.read_csv("C:/Users/rushi/OneDrive/Desktop/Personal Project/IPL Aution Analysis-2023/IPL_2023-22_Sold_Players.csv")
In [3]:
df
Out[3]:
Season Name Nationality Type Team Price
0 2023 Ajinkya Rahane Indian Batter Chennai Super Kings 50,00,000
1 2023 Bhagath Varma Indian All-Rounder Chennai Super Kings 20,00,000
2 2023 Kyle Jamieson Overseas Bowler Chennai Super Kings 1,00,00,000
3 2023 Ajay Mandal Indian All-Rounder Chennai Super Kings 20,00,000
4 2023 Nishant Sindhu Indian All-Rounder Chennai Super Kings 60,00,000
... ... ... ... ... ... ...
279 2022 Fazalhaq Farooqi Overseas Bowler Sunrisers Hyderabad 50,00,000
280 2022 Sean Abbott Overseas Bowler Sunrisers Hyderabad 2,40,00,000
281 2022 R Samarth Indian Batsman Sunrisers Hyderabad 20,00,000
282 2022 Shashank Singh Indian All-Rounder Sunrisers Hyderabad 20,00,000
283 2022 Saurabh Dubey Indian Bowler Sunrisers Hyderabad 20,00,000

284 rows × 6 columns

In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 284 entries, 0 to 283
Data columns (total 6 columns):
 #   Column       Non-Null Count  Dtype 
---  ------       --------------  ----- 
 0   Season       284 non-null    int64 
 1   Name         284 non-null    object
 2   Nationality  284 non-null    object
 3   Type         284 non-null    object
 4   Team         284 non-null    object
 5   Price        284 non-null    object
dtypes: int64(1), object(5)
memory usage: 13.4+ KB

Removing Comma (,) in price¶

In [5]:
df['Price'] = df['Price'].apply(lambda x:str(x).replace(',','')).astype('float')
In [6]:
df['Price']
Out[6]:
0       5000000.0
1       2000000.0
2      10000000.0
3       2000000.0
4       6000000.0
          ...    
279     5000000.0
280    24000000.0
281     2000000.0
282     2000000.0
283     2000000.0
Name: Price, Length: 284, dtype: float64

Number of Unique Values¶

In [7]:
for col in df.columns:
    print('Number of Unique Values in', col,'are',df[col].nunique())

Number of Unique Values in Season are 2
Number of Unique Values in Name are 266
Number of Unique Values in Nationality are 2
Number of Unique Values in Type are 6
Number of Unique Values in Team are 10
Number of Unique Values in Price are 66

FEATURE ENGINEERING¶

In [8]:
df['Type'].unique()
Out[8]:
array(['Batter ', 'All-Rounder ', 'Bowler ', 'Wicket-Keeper ', 'Batsman ',
       'Wicket Keeper '], dtype=object)

Replacing Different Name To Unique Name¶

In [9]:
df['Type']=df['Type'].apply(lambda x:x.replace('Batter ','Batsman'))
df['Type']=df['Type'].apply(lambda x:x.replace('Batsman ','Batsman'))
df['Type']=df['Type'].apply(lambda x:x.replace('All-Rounder ','All-Rounder'))
df['Type']=df['Type'].apply(lambda x:x.replace(' All-Rounder ','All-Rounder'))
df['Type']=df['Type'].apply(lambda x:x.replace(' All-Rounder','All-Rounder'))
df['Type']=df['Type'].apply(lambda x:x.replace('Bowler','Bowler'))
df['Type']=df['Type'].apply(lambda x:x.replace('Bowler ','Bowler'))
df['Type']=df['Type'].apply(lambda x:x.replace(' Bowler','Bowler'))
df['Type']=df['Type'].apply(lambda x:x.replace('Wicket-Keeper ','Wicket-Keeper'))
df['Type']=df['Type'].apply(lambda x:x.replace('Wicket Keeper ','Wicket-Keeper'))
df['Type']=df['Type'].apply(lambda x:x.replace(' Wicket Keeper','Wicket-Keeper'))
df['Type']=df['Type'].apply(lambda x:x.replace('Wicket Keeper','Wicket-Keeper'))
In [10]:
df['Type'].unique()
Out[10]:
array(['Batsman', 'All-Rounder', 'Bowler', 'Wicket-Keeper'], dtype=object)

Checking Duplicate Values¶

In [11]:
df.duplicated().sum()
Out[11]:
0
In [12]:
df['Nationality'].value_counts()
Out[12]:
Indian       188
Overseas      96
Name: Nationality, dtype: int64

418 VALUES RE NOT AVIALABLE WE CANNOT REPLCE WITH ANY OTHER VALUES SO,JUST KEEPING IT AS SAME AND IT IS NOT IDENTIFIED AS A NULL VALUE

In [13]:
for col in df.columns:
    print('No. of Null Values in',col, 'are', df[col].isnull().sum() )

No. of Null Values in Season are 0
No. of Null Values in Name are 0
No. of Null Values in Nationality are 0
No. of Null Values in Type are 0
No. of Null Values in Team are 0
No. of Null Values in Price are 0
In [14]:
df.groupby('Nationality')['Price'].mean()
Out[14]:
Nationality
Indian       1.894415e+07
Overseas     3.776562e+07
Name: Price, dtype: float64

OVERSEAS PLAYERS GETTING HIGH PAY COMPARED TOOUR INDIAN PLAYERS

In [15]:
df['Nationality'] = df['Nationality'].apply(lambda x:'na' if "Not Available" in x else x)

DATA VISUALISATION¶

SWARMPLOT IS LIKE SCATTER PLOT SO, WE CAN OBSERVE HOW DATA POINTS ARE DISTRIBUTED

In [16]:
sns.swarmplot(x = "Nationality", y="Price", data=df)

C:\Users\rushi\anaconda3\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 45.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
Out[16]:
<AxesSubplot:xlabel='Nationality', ylabel='Price'>
In [17]:
df = df.sort_values(by='Price',ascending=False)
df.head(10)
Out[17]:
Season Name Nationality Type Team Price
49 2023 Sam Curran Overseas All-Rounder Punjab Kings 185000000.0
40 2023 Cameron Green Overseas All-Rounder Mumbai Indians 175000000.0
6 2023 Ben Stokes Overseas All-Rounder Chennai Super Kings 162500000.0
28 2023 Nicholas Pooran Overseas Wicket-Keeper Lucknow Super Giants 160000000.0
180 2022 Ishan Kishan Indian Wicket-Keeper Mumbai Indians 152500000.0
83 2022 Deepak Chahar Indian Bowler Chennai Super Kings 140000000.0
68 2023 Harry Brook Overseas Batsman Sunrisers Hyderabad 132500000.0
142 2022 Shreyas Iyer Indian Batsman Kolkata Knight Riders 122500000.0
210 2022 Liam Livingstone Overseas All-Rounder Punjab Kings 115000000.0
246 2022 Wanindu Hasaranga Overseas All-Rounder Royal Challengers Bangalore 107500000.0

THESE ARE THE TOP PLAYERS IN IPL

SAM CURRAN IS THE HIGHEST PAID PLAYER IN IPL HISTORY FROM 2013 TO 2023

In [18]:
df.groupby('Type')['Price'].mean()
Out[18]:
Type
All-Rounder      2.680189e+07
Batsman          2.726042e+07
Bowler           2.166667e+07
Wicket-Keeper    2.816176e+07
Name: Price, dtype: float64

WICKET KEEPERS AND ALL ROUNDERS ARE GETTING NEARLY EQUAL AMOUNT OF AVERAGE PAY of 2 CRORES

In [19]:
top_10_paid = (df.groupby('Name')['Price'].mean().sort_values(ascending=False)).head(10)
top_10_paid
Out[19]:
Name
Sam Curran           185000000.0
Cameron Green        175000000.0
Ben Stokes           162500000.0
Ishan Kishan         152500000.0
Deepak Chahar        140000000.0
Nicholas Pooran      133750000.0
Harry Brook          132500000.0
Shreyas Iyer         122500000.0
Liam Livingstone     115000000.0
Harshal Patel        107500000.0
Name: Price, dtype: float64
In [20]:
fig = px.bar(x=top_10_paid.index,
             y=top_10_paid.values,
            color=top_10_paid.index,
            text=top_10_paid.values,
            title='Top_Paid_Cricketers')
fig.update_layout(xaxis_title='Players',yaxis_title='Amount')
In [21]:
features = ['Name', 'Nationality', 'Type', 'Team']
for feature in features:
    Contineuts_population=df.groupby(by=feature)['Price'].mean().sort_values(ascending=False).head(7)
    fig = px.bar(x=Contineuts_population.index,
                 y=Contineuts_population.values,
                 color=Contineuts_population,
                 text=Contineuts_population.values,
                 color_discrete_sequence=px.colors.sequential.PuBu,
                 template='plotly_dark')
    fig.update_layout(xaxis_title=feature,yaxis_title='Payment paid')
    fig.show()

WE CAN ANALSE

1)FOREIGN PLAYERS PAID HIGH COMPARED TO INDIAN PLAYERS

2)BOWLERS ARE GETTING LOW PAY COMPARING TO REMAINING

3)PUNJAB KINGS UTILISED MOST OF IT'S MONEY ON PLAYERS

In [22]:
lowest_paid_players=(df.groupby('Name')['Price'].mean().sort_values(ascending=True)).head(10)
lowest_paid_players
Out[22]:
Name
Abdul P A             2000000.0
Mukesh Choudhary      2000000.0
Mohsin Khan           2000000.0
Mohit Rathee          2000000.0
Mohd. Arshad Khan     2000000.0
Tejas Baroka          2000000.0
Mayank Yadav          2000000.0
Yudhvir Charak        2000000.0
Manoj Bhandage        2000000.0
Manan Vohra           2000000.0
Name: Price, dtype: float64
In [23]:
fig=px.bar(x=lowest_paid_players.index,
           y=lowest_paid_players.values,
           color= lowest_paid_players.index,
           text= lowest_paid_players.values,
           title= 'Lowest Paid Players')
fig.update_layout(xaxis_title='Players', yaxis_title='Amount')
In [24]:
fig=px.bar(x=lowest_paid_players.index,
           y= lowest_paid_players.values,
           color= lowest_paid_players.index,
           text= lowest_paid_players.values,
           title=' lowest_paid_players')
fig.update_layout(xaxis_title='Players',yaxis_title='Amount')

ABOVE ARE THE LOWEST PAID PLAYERS¶

Thank You¶